serving
serving at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Master MoE Training with a Practical Megatron-LM Runbook”, “Explore the LLM Frameworks Shaping Smarter Production Deployments”, and “A free guide to making ML models faster, from silicon to agents”. Training a sparse Mixture-of-Experts model after a dense baseline can feel like stepping into a different world. Comparing LLM frameworks for production can feel like navigating a maze of technical trade-offs. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every serving story on Beyond Market Intelligence, newest first.
Master MoE Training with a Practical Megatron-LM Runbook
Training a sparse Mixture-of-Experts model after a dense baseline can feel like stepping into a different world. This handbook from Hugging Face's Nouamane and colleagues demystifies that leap with a practical Megatron-LM runbook: test hardware, build the image, train a dense model, then scale to a single-node MoE. Chapters on the router, load balancing, and serving require no GPU. The insights draw from roughly 800k GH200 GPU-hours on CSCS's Alps cluster, with every claim clearly sourced.
Explore the LLM Frameworks Shaping Smarter Production Deployments
Comparing LLM frameworks for production can feel like navigating a maze of technical trade-offs. We cut through the noise to compare inference frameworks on request batching, KV cache management, and efficient deployment. Our goal is to make these complex systems accessible so you can choose the right tool for smarter production serving. For a different kind of data challenge, explore our related coding puzzle, "Quest 6: The Nullspiracy," which transforms spreadsheet puzzles into a discovery journey.
A free guide to making ML models faster, from silicon to agents
Performance engineering isn't just about reducing FLOPs. Usamah Zakir spent months writing the guide he wished he'd had when starting out: *How to Make Your Model Fast*. It moves from silicon to agents, using roofline analysis to help you diagnose whether you're compute, bandwidth, or memory bound before optimizing. That systems-first thinking is rare and valuable. The whole thing is free and open source. If you're working on inference or edge AI, it's worth exploring.